Search

Public health and its related facilities are crucial for thriving cities and societies. The optimum utilization of health resources saves money and time, but above all, it saves precious lives. It has become even more evident in the present as the pandemic has overstretched the existing medical resources. Specific to patient appointment scheduling, the casual attitude of missing medical appointments (no-show-ups) may cause severe damage to a patient's health. In this paper, with the help of machine learning, we analyze six million plus patient appointment records to predict a patient's behaviors/characteristics by using ten different machine learning algorithms. For this purpose, we first extracted meaningful features from raw data using data cleaning. We applied Synthetic Minority Oversampling Technique (SMOTE), Adaptive Synthetic Sampling Method (Adasyn), and random undersampling (RUS) to balance our data. After balancing, we applied ten different machine learning algorithms, namely, random forest classifier, decision tree, logistic regression, XG Boost, gradient boosting, Adaboost Classifier, Naive Bayes, stochastic gradient descent, multilayer perceptron, and Support Vector Machine. We analyzed these results with the help of six different metrics, i.e., recall, accuracy, precision, F1-score, area under the curve, and mean square error. Our study has achieved 94% recall, 86% accuracy, 83% precision, 87% F1-score, 92% area under the curve, and 0.106 minimum mean square error. Effectiveness of presented data cleaning and feature selection is confirmed by better results in all training algorithms. Notably, recall is greater than 75%, accuracy is greater than 73%, F1-score is more significant than 75%, MSE is lesser than 0.26, and AUC is greater than 74%. The research shows that instead of individual features, combining different features helps make better predictions of a patient's appointment status., Competing Interests: The authors declare that there is no conflict of interest regarding the publication of this paper., (Copyright © 2021 Zeeshan Qureshi et al.)

Due to the inability of the Global Positioning System (GPS) signals to penetrate through surfaces like roofs, walls, and other objects in indoor environments, numerous alternativemethods for user positioning have been presented. Amongst those, the Wi-Fi fingerprinting method has gained considerable interest in Indoor Positioning Systems (IPS) as the need for lineof-sight measurements is minimal, and it achieves better efficiency in even complex indoor environments. Offline and online are the two phases of the fingerprinting method. Many researchers have highlighted the problems in the offline phase as it deals with huge datasets and validation of Fingerprints without pre-processing of data becomes a concern. Machine learning is used for the model training in the offline phase while the locations are estimated in the online phase. Many researchers have considered the concerns in the offline phase as it deals with huge datasets and validation of Fingerprints becomes an issue. Machine learning algorithms are a natural solution for winnowing through large datasets and determining the significant fragments of information for localization, creating precise models to predict an indoor location. Large training sets are a key for obtaining better results in machine learning problems. Therefore, an existing WLAN fingerprinting-based multistory building location database has been used with 21049 samples including 19938 training and 1111 testing samples. The proposed model consists of mean and median filtering as pre-processing techniques applied to the database for enhancing the accuracy by mitigating the impact of environmental dispersion and investigated machine learning algorithms (kNN, WkNN, FSkNN, and SVM) for estimating the location. The proposed SVM with median filtering algorithm gives a reduced mean positioning error of 0.7959mand an improved efficiency of 92.84% as compared to all variants of the proposed method for 108703 m2 area. [ABSTRACT FROM AUTHOR]

Social distancing is an utmost reliable practice to minimise the spread of coronavirus disease (COVID-19). As the new variant of COVID-19 is emerging, healthcare organisations are concerned with controlling the death and infection rates. Different COVID-19 vaccines have been developed and administered worldwide. However, presently developed vaccine quantity is not sufficient to fulfil the needs of the world's population. The precautionary measures still rely on personal preventive strategies. The sharp rise in infections has forced governments to reimpose restrictions. Governments are forcing people to maintain at least 6 feet (ft) of safe physical distance to stay safe. With summers, low-light conditions can become challenging. Especially in the cities of underdeveloped countries, where poor ventilated and congested homes cause people to gather in open spaces such as parks, streets, and markets. Besides this, in summer, large friends and family gatherings mostly take place at night. It is necessary to take precautionary measures to avoid more drastic results in such situations. To support the law and order bodies in maintaining social distancing using Social Internet of Things (SIoT), the world is considering automated systems. To address the identification of violations of a social distancing Standard Operating procedure (SOP) in low-light environments via smart, automated cyber-physical solutions, we propose an effective social distance monitoring approach named DepTSol. We propose a low-cost and easy-to-maintain motionless monocular time-of-flight (ToF) camera and deep-learning-based object detection algorithms for real-time social distance monitoring. The proposed approach detects people in low-light environments and calculates their distance in terms of pixels. We convert the predicted pixel distance into real-world units and compare it with the specified safety threshold value. The system highlights people violating the safe distance. The proposed technique is evaluated by COCO evaluation metrics and has achieved a good speed–accuracy trade-off with 51.2 frames per second (fps) and a 99.7% mean average precision (mAP) score. Besides the provision of an effective social distance monitoring approach, we perform a comparative analysis between one-stage object detectors and evaluate their performance in low-light environments. This evaluation will pave the way for researchers to study the field further and will enlighten the efficiency of deep-learning algorithms in timely responsive real-world applications. [ABSTRACT FROM AUTHOR]

The non-linearity in medical image processing is a critical issue. Because the privacy of the medical image and loss of data is a major concern in recent years. Federated learning is a most advanced form of machine learning in which, rather than transmitting data to local server, a machine learning (ML) algorithm is installed to various devices to train on the information. The parameters from the separate modules will then be transferred to a master ML/ (deep learning) DL model for global training. The research of Image Quality Assessment (IQA) aims to simulate the process of human perception of image quality and construct an objective image quality model as consistent as possible with subjective assessment. The existing IQA methods can be roughly divided into traditional methods and deep learning methods. Traditional methods are knowledge-driven, using prior knowledge or assumptions about the human visual system (HVS) to heuristically design image quality index. Deep learning methods are data-driven, using a large amount of annotated data to learn the mapping from the image to its visual quality end-to-end. To effectively integrate traditional methods into deep networks and investigate the knowledge (model)-driven deep learning methods are the current mainstream trends in IQA research. In this paper, we take the contrary direction and improve traditional methods guided by the cue from deep learning methods. The main works include: 1. the employment of activation function ensure the nonlinear approximation ability of the neural network, here we first extend the two-stage framework widely used in the field of full-reference image quality assessment and propose a nonlinear two-stage framework. 2. Within this framework, we revisit the Edge Strength SIMlarity (ESSIM) algorithm that we previously published in IEEE Signal Processing Letters, and proposed the Nonlinear Edge Strength SIMlarity (NESSIM) algorithm. Experiments on public databases show that NESSIM can obtain good assessment results in traditional methods. • Improve traditional methods guided by the cue from deep learning methods. • Employment of activation function ensure the nonlinear approximation ability of the neural network. • Propose a nonlinear two-stage framework • Proposed the Nonlinear Edge Strength SIMlarity (NESSIM) algorithm. [ABSTRACT FROM AUTHOR]